{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Political Alignment and Polarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "This is the second in a series of notebooks that make up a [case study in exploratory data analysis](https://allendowney.github.io/PoliticalAlignmentCaseStudy/).\n",
    "This case study is part of the [*Elements of Data Science*](https://allendowney.github.io/ElementsOfDataScience/) curriculum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chapter and the next make up a case study that uses data from the General Social Survey (GSS) to explore political beliefs and political alignment (conservative, moderate, or liberal) in the United States.\n",
    "\n",
    "In this chapter, we:\n",
    "\n",
    "1. Compare the distributions of political alignment from 1974 and 2021.\n",
    "\n",
    "2. Plot the mean and standard deviation of responses over time as a way of quantifying shifts in political alignment and polarization.\n",
    "\n",
    "3. Use local regression to plot a smooth line through noisy data.\n",
    "\n",
    "4. Use cross tabulation to compute the fraction of respondents in each category over time.\n",
    "\n",
    "5. Plot the results using a custom color palette.\n",
    "\n",
    "As an exercise, you will look at changes in political party affiliation over the same period.\n",
    "\n",
    "In the next chapter, we'll use the same dataset to explore the relationship between political alignment and other attitudes and beliefs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "The following cell installs the `empiricaldist` library if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import empiricaldist\n",
    "except ImportError:\n",
    "    !pip install empiricaldist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "If everything we need is installed, the following cell should run without error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from empiricaldist import Pmf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "## Loading the data\n",
    "\n",
    "In the previous notebook, we downloaded GSS data, loaded and cleaned it, resampled it to correct for stratified sampling, and then saved the data in an HDF file, which is much faster to load.  In this and the following notebooks, we'll download the HDF file and load it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "The following cell downloads the file if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "from os.path import basename, exists\n",
    "\n",
    "\n",
    "def download(url):\n",
    "    filename = basename(url)\n",
    "    if not exists(filename):\n",
    "        from urllib.request import urlretrieve\n",
    "\n",
    "        local, _ = urlretrieve(url, filename)\n",
    "        print(\"Downloaded \" + local)\n",
    "\n",
    "\n",
    "download(\n",
    "    \"https://github.com/AllenDowney/PoliticalAlignmentCaseStudy/raw/master/gss_pacs_resampled.hdf\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the the repository for this book, you'll find an HDF file that contains the GSS data, which I have cleaned and resampled to correct for stratified sampling.\n",
    "The file that contains three resamplings;  we'll use the first, `gss0`, to get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68846, 204)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafile = \"gss_pacs_resampled.hdf\"\n",
    "gss = pd.read_hdf(datafile, \"gss0\")\n",
    "gss.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Political Alignment\n",
    "\n",
    "The people surveyed as part of the GSS were asked about their \"political alignment\", which is where they place themselves on a spectrum from liberal to conservative.\n",
    "\n",
    "The variable `polviews` contains responses to the following question (see <https://gssdataexplorer.norc.org/variables/178/vshow>):\n",
    "\n",
    "> We hear a lot of talk these days about liberals and conservatives. \n",
    "I'm going to show you a seven-point scale on which the political views that people might hold are arranged from extremely liberal--point 1--to extremely conservative--point 7. Where would you place yourself on this scale?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the valid responses:\n",
    "\n",
    "```\n",
    "1\tExtremely liberal\n",
    "2\tLiberal\n",
    "3\tSlightly liberal\n",
    "4\tModerate\n",
    "5\tSlightly conservative\n",
    "6\tConservative\n",
    "7\tExtremely conservative\n",
    "```\n",
    "\n",
    "To see how the responses have changed over time, we'll inspect them at the beginning and end of the observation period.\n",
    "First we'll select the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "polviews = gss[\"polviews\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can compute a Boolean Series that's `True` for responses from 1974."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "year74 = gss[\"year\"] == 1974"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can select the responses from 1974."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "polviews74 = polviews[year74]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the following function to count the number of times each response occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def values(series):\n",
    "    \"\"\"Count the values and sort.\n",
    "\n",
    "    series: pd.Series\n",
    "\n",
    "    returns: series mapping from values to frequencies\n",
    "    \"\"\"\n",
    "    return series.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the responses from 1974."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     31\n",
       "2.0    201\n",
       "3.0    211\n",
       "4.0    538\n",
       "5.0    223\n",
       "6.0    181\n",
       "7.0     30\n",
       "Name: polviews, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values(polviews74)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here are the responses from 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     212\n",
       "2.0     577\n",
       "3.0     427\n",
       "4.0    1506\n",
       "5.0     480\n",
       "6.0     569\n",
       "7.0     181\n",
       "Name: polviews, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year21 = gss[\"year\"] == 2021\n",
    "polviews21 = polviews[year21]\n",
    "values(polviews21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at a table of counts, we can get a sense of what the distribution looks like, but in the next section we'll get a better sense by plotting it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Distributions\n",
    "\n",
    "To visualize these distributions, we'll use a Probability Mass Function (PMF), which is similar to a histogram, but there are two differences:\n",
    "\n",
    "* In a histogram, values are often put in bins, with more than one value in each bin. In a PMF each value gets its own bin.\n",
    "\n",
    "* A histogram computes a count, that is, how many times each value appears; a PMF computes a probability, that is, what fraction of the time each value appears. \n",
    "\n",
    "We'll use the `Pmf` class from `empiricaldist` to compute a PMF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.021908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.142049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.149117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.380212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.157597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.127915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.021201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "1.0    0.021908\n",
       "2.0    0.142049\n",
       "3.0    0.149117\n",
       "4.0    0.380212\n",
       "5.0    0.157597\n",
       "6.0    0.127915\n",
       "7.0    0.021201\n",
       "Name: , dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from empiricaldist import Pmf\n",
    "\n",
    "pmf74 = Pmf.from_seq(polviews74)\n",
    "pmf74"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "The following cell defines a function I use to decorate the axes in plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def decorate(**options):\n",
    "    \"\"\"Decorate the current axes.\n",
    "\n",
    "    Call decorate with keyword arguments like\n",
    "    decorate(title='Title',\n",
    "             xlabel='x',\n",
    "             ylabel='y')\n",
    "\n",
    "    The keyword arguments can be any of the axis properties\n",
    "    https://matplotlib.org/api/axes_api.html\n",
    "    \"\"\"\n",
    "    ax = plt.gca()\n",
    "    ax.set(**options)\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    if handles:\n",
    "        ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution from 1974:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmf74.bar(label=\"1974\", color=\"C0\", alpha=0.7)\n",
    "\n",
    "decorate(\n",
    "    xlabel=\"Political view on a 7-point scale\",\n",
    "    ylabel=\"Fraction of respondents\",\n",
    "    title=\"Distribution of political views\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And from 2021:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmf21 = Pmf.from_seq(polviews21)\n",
    "pmf21.bar(label=\"2021\", color=\"C1\", alpha=0.7)\n",
    "\n",
    "decorate(\n",
    "    xlabel=\"Political view on a 7-point scale\",\n",
    "    ylabel=\"Fraction of respondents\",\n",
    "    title=\"Distribution of political views\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In both cases, the most common response is `4`, which is the code for \"moderate\".  Few respondents describe themselves as \"extremely\" liberal or conservative.\n",
    "So maybe we're not so polarized after all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "To make it easier to compare the distributions, I'll plot them side by side.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = dict(pmf74=pmf74, pmf21=pmf21)\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df.plot(kind=\"bar\")\n",
    "\n",
    "decorate(\n",
    "    xlabel=\"Political view on a 7-point scale\",\n",
    "    ylabel=\"Fraction of respondents\",\n",
    "    title=\"Distribution of political views\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "Now we can see the changes in the distribution more clearly.  It looks like the number of people at the extremes (1 and 7) has increased, and the fraction of slightly liberal (3) and slightly conservative (5) has decreased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** To summarize these changes, we can compare the mean and standard deviation of `polviews` in 1974 and 2021.\n",
    "\n",
    "The mean of the responses measures the balance of people in the population with liberal or conservative leanings.  If the mean increases over time, that might indicate a shift in the population toward conservatism.\n",
    "\n",
    "The standard deviation measures the dispersion of views in the population; if it increases over time, that might indicate an increase in polarization.\n",
    "\n",
    "Compute the mean and standard deviation of `polviews74` and `polviews21`.\n",
    "\n",
    "What do they indicate about changes over this interval?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.9780918727915195, 1.3324162225592249)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "polviews74.mean(), polviews74.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.98582995951417, 1.5115522818003158)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "polviews21.mean(), polviews21.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# The mean has barely changed, which indicates no\n",
    "# net shift toward liberal or conservate identity.\n",
    "\n",
    "# The standard deviation has increased,\n",
    "# which suggests increased polarization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Time Series\n",
    "\n",
    "At this point we have looked at the endpoints, 1974 and 2021, but we don't know what happened in between.\n",
    "To see how the distribution changed over time, we can group by year and compute the mean of `polviews` during each year.\n",
    "We can use `groupby` to group the respondents by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fbff798f5e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss_by_year = gss.groupby(\"year\")\n",
    "gss_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `DataFrameGroupBy` object that represents a collection of groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "We can loop through the groups and display the number of respondents in each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1972 1613\n",
      "1973 1504\n",
      "1974 1484\n",
      "1975 1490\n",
      "1976 1499\n",
      "1977 1530\n",
      "1978 1532\n",
      "1980 1468\n",
      "1982 1860\n",
      "1983 1599\n",
      "1984 1473\n",
      "1985 1534\n",
      "1986 1470\n",
      "1987 1819\n",
      "1988 1481\n",
      "1989 1537\n",
      "1990 1372\n",
      "1991 1517\n",
      "1993 1606\n",
      "1994 2992\n",
      "1996 2904\n",
      "1998 2832\n",
      "2000 2817\n",
      "2002 2765\n",
      "2004 2812\n",
      "2006 4510\n",
      "2008 2023\n",
      "2010 2044\n",
      "2012 1974\n",
      "2014 2538\n",
      "2016 2867\n",
      "2018 2348\n",
      "2021 4032\n"
     ]
    }
   ],
   "source": [
    "for year, group in gss_by_year:\n",
    "    print(year, len(group))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many ways the `DataFrameGroupBy` behaves like a `DataFrame`.  We can use the bracket operator to select a column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fbff798f820>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polviews_by_year = gss_by_year[\"polviews\"]\n",
    "polviews_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A column from a `DataFrameGroupBy` is a `SeriesGroupBy`.  If we invoke `mean` on it, the results is a series that contains the mean of `polviews` for each year of the survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_series = polviews_by_year.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_series.plot(color=\"C2\", label=\"polviews\")\n",
    "decorate(xlabel=\"Year\", ylabel=\"Mean (7 point scale)\", title=\"Mean of polviews\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the mean increased between 1974 and 2000, decreased since then, and ended up almost where it started.\n",
    "The difference between the highest and lowest points is only 0.3 points on a 7-point scale, which is a modest effect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34240143126104083"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_series.max() - mean_series.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The standard deviation quantifies the spread of the distribution, which is one way to measure polarization.\n",
    "Plot standard deviation of `polviews` for each year of the survey from 1972 to 2021.\n",
    "Does it show evidence of increasing polarization?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_series = gss_by_year[\"polviews\"].std()\n",
    "std_series.plot(color=\"C4\", label=\"polviews\")\n",
    "decorate(\n",
    "    ylabel=\"Standard deviation (7 point scale)\", title=\"Standard deviation of polviews\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# The standard deviation has increased consistently over the entire interval.\n",
    "# But the difference between the highest and lowest points is small.\n",
    "# So if polarization is increasing, it is not a recent phenomenon, and it\n",
    "# is not changing quickly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Smoothing the Curve\n",
    "\n",
    "In the previous section we plotted mean and standard deviation of `polviews` over time.\n",
    "In both plots, the values are highly variable from year to year.\n",
    "We can use **local regression** to compute a smooth line through these data points.  \n",
    "\n",
    "The following function takes a Pandas Series and uses an algorithm called LOWESS to compute a smooth line.\n",
    "LOWESS stands for \"locally weighted scatterplot smoothing\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
    "\n",
    "\n",
    "def make_lowess(series):\n",
    "    \"\"\"Use LOWESS to compute a smooth line.\n",
    "\n",
    "    series: pd.Series\n",
    "\n",
    "    returns: pd.Series\n",
    "    \"\"\"\n",
    "    y = series.values\n",
    "    x = series.index.values\n",
    "\n",
    "    smooth = lowess(y, x)\n",
    "    index, data = np.transpose(smooth)\n",
    "\n",
    "    return pd.Series(data, index=index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the following function to plot data points and the smoothed line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_series_lowess(series, color):\n",
    "    \"\"\"Plots a series of data points and a smooth line.\n",
    "\n",
    "    series: pd.Series\n",
    "    color: string or tuple\n",
    "    \"\"\"\n",
    "    series.plot(linewidth=0, marker=\"o\", color=color, alpha=0.5)\n",
    "    smooth = make_lowess(series)\n",
    "    smooth.plot(label=\"\", color=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure shows the mean of `polviews` and a smooth line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_series = gss_by_year[\"polviews\"].mean()\n",
    "plot_series_lowess(mean_series, \"C2\")\n",
    "decorate(ylabel=\"Mean (7 point scale)\", title=\"Mean of polviews\", xlabel=\"Year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One reason the PMFs for 1974 and 2021 did not look very different is that the mean seems to have gone up (more conservative) and then down again (more liberal).\n",
    "Generally, it looks like the U.S. has been trending toward liberal for the last 20 years, or more, at least in the sense of how people describe themselves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Use `plot_series_lowess` to plot the standard deviation of `polviews` with a smooth line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_series = gss_by_year[\"polviews\"].std()\n",
    "plot_series_lowess(std_series, color=\"C4\")\n",
    "decorate(\n",
    "    ylabel=\"Standard deviation (7 point scale)\",\n",
    "    title=\"Standard deviation of polviews\",\n",
    "    xlabel=\"Year\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Tabulation\n",
    "\n",
    "In the previous sections, we treated `polviews` as a numerical quantity, so we were able to compute means and standard deviations.\n",
    "But the responses are really categorical, which means that each value represents a discrete category, like \"liberal\" or \"conservative\".\n",
    "In this section, we'll treat `polviews` as a categorical variable.  Specifically, we'll compute the number of respondents in each category for each year, and plot changes over time.\n",
    "\n",
    "Pandas provides a function called `crosstab` that computes a **cross tabulation**, which is like a two-dimensional PMF.\n",
    "It takes two `Series` objects as arguments and returns a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = gss[\"year\"]\n",
    "column = gss[\"polviews\"]\n",
    "\n",
    "xtab = pd.crosstab(year, column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the first few lines from the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>polviews</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>31</td>\n",
       "      <td>201</td>\n",
       "      <td>211</td>\n",
       "      <td>538</td>\n",
       "      <td>223</td>\n",
       "      <td>181</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>56</td>\n",
       "      <td>184</td>\n",
       "      <td>207</td>\n",
       "      <td>540</td>\n",
       "      <td>204</td>\n",
       "      <td>162</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>31</td>\n",
       "      <td>198</td>\n",
       "      <td>175</td>\n",
       "      <td>564</td>\n",
       "      <td>209</td>\n",
       "      <td>206</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>37</td>\n",
       "      <td>181</td>\n",
       "      <td>214</td>\n",
       "      <td>594</td>\n",
       "      <td>243</td>\n",
       "      <td>164</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>255</td>\n",
       "      <td>559</td>\n",
       "      <td>265</td>\n",
       "      <td>187</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "polviews  1.0  2.0  3.0  4.0  5.0  6.0  7.0\n",
       "year                                       \n",
       "1974       31  201  211  538  223  181   30\n",
       "1975       56  184  207  540  204  162   45\n",
       "1976       31  198  175  564  209  206   34\n",
       "1977       37  181  214  594  243  164   42\n",
       "1978       21  140  255  559  265  187   25"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtab.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It contains one row for each value of `year` and one column for each value of `polviews`.  Reading the first row, we see that in 1974, 31 people gave response 1, \"extremely liberal\", 201 people gave response 2, \"liberal\", and so on.\n",
    "\n",
    "The number of respondents varies from year to year, so we need to normalize the results, which means computing for each year the *fraction* of respondents in each category, rather than the count.\n",
    "\n",
    "`crosstab` takes an optional argument that normalizes each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtab_norm = pd.crosstab(year, column, normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what that looks like for the 7-point scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>polviews</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>0.021908</td>\n",
       "      <td>0.142049</td>\n",
       "      <td>0.149117</td>\n",
       "      <td>0.380212</td>\n",
       "      <td>0.157597</td>\n",
       "      <td>0.127915</td>\n",
       "      <td>0.021201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>0.040057</td>\n",
       "      <td>0.131617</td>\n",
       "      <td>0.148069</td>\n",
       "      <td>0.386266</td>\n",
       "      <td>0.145923</td>\n",
       "      <td>0.115880</td>\n",
       "      <td>0.032189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>0.021877</td>\n",
       "      <td>0.139732</td>\n",
       "      <td>0.123500</td>\n",
       "      <td>0.398024</td>\n",
       "      <td>0.147495</td>\n",
       "      <td>0.145378</td>\n",
       "      <td>0.023994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>0.025085</td>\n",
       "      <td>0.122712</td>\n",
       "      <td>0.145085</td>\n",
       "      <td>0.402712</td>\n",
       "      <td>0.164746</td>\n",
       "      <td>0.111186</td>\n",
       "      <td>0.028475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.014463</td>\n",
       "      <td>0.096419</td>\n",
       "      <td>0.175620</td>\n",
       "      <td>0.384986</td>\n",
       "      <td>0.182507</td>\n",
       "      <td>0.128788</td>\n",
       "      <td>0.017218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "polviews       1.0       2.0       3.0       4.0       5.0       6.0       7.0\n",
       "year                                                                          \n",
       "1974      0.021908  0.142049  0.149117  0.380212  0.157597  0.127915  0.021201\n",
       "1975      0.040057  0.131617  0.148069  0.386266  0.145923  0.115880  0.032189\n",
       "1976      0.021877  0.139732  0.123500  0.398024  0.147495  0.145378  0.023994\n",
       "1977      0.025085  0.122712  0.145085  0.402712  0.164746  0.111186  0.028475\n",
       "1978      0.014463  0.096419  0.175620  0.384986  0.182507  0.128788  0.017218"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtab_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the numbers in the table, it's hard to see what's going on.\n",
    "In the next section, we'll plot the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "To make the results easier to interpret, I'm going to replace the numeric codes 1-7 with strings.  First I'll make a dictionary that maps from numbers to strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [],
   "source": [
    "# recode the 7 point scale with words\n",
    "d7 = {\n",
    "    1: \"Extremely liberal\",\n",
    "    2: \"Liberal\",\n",
    "    3: \"Slightly liberal\",\n",
    "    4: \"Moderate\",\n",
    "    5: \"Slightly conservative\",\n",
    "    6: \"Conservative\",\n",
    "    7: \"Extremely conservative\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "Then we can use the `replace` function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [],
   "source": [
    "polviews7 = gss[\"polviews\"].replace(d7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "We can use `values` to confirm that the values in `polviews7` are strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Conservative               9064\n",
       "Extremely conservative     1951\n",
       "Extremely liberal          1911\n",
       "Liberal                    6876\n",
       "Moderate                  22950\n",
       "Slightly conservative      9344\n",
       "Slightly liberal           7408\n",
       "Name: polviews, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values(polviews7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "If we make the cross tabulation again, we can see that the column names are strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>polviews</th>\n",
       "      <th>Conservative</th>\n",
       "      <th>Extremely conservative</th>\n",
       "      <th>Extremely liberal</th>\n",
       "      <th>Liberal</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Slightly conservative</th>\n",
       "      <th>Slightly liberal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>0.127915</td>\n",
       "      <td>0.021201</td>\n",
       "      <td>0.021908</td>\n",
       "      <td>0.142049</td>\n",
       "      <td>0.380212</td>\n",
       "      <td>0.157597</td>\n",
       "      <td>0.149117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>0.115880</td>\n",
       "      <td>0.032189</td>\n",
       "      <td>0.040057</td>\n",
       "      <td>0.131617</td>\n",
       "      <td>0.386266</td>\n",
       "      <td>0.145923</td>\n",
       "      <td>0.148069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>0.145378</td>\n",
       "      <td>0.023994</td>\n",
       "      <td>0.021877</td>\n",
       "      <td>0.139732</td>\n",
       "      <td>0.398024</td>\n",
       "      <td>0.147495</td>\n",
       "      <td>0.123500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>0.111186</td>\n",
       "      <td>0.028475</td>\n",
       "      <td>0.025085</td>\n",
       "      <td>0.122712</td>\n",
       "      <td>0.402712</td>\n",
       "      <td>0.164746</td>\n",
       "      <td>0.145085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.128788</td>\n",
       "      <td>0.017218</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>0.096419</td>\n",
       "      <td>0.384986</td>\n",
       "      <td>0.182507</td>\n",
       "      <td>0.175620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "polviews  Conservative  Extremely conservative  Extremely liberal   Liberal  \\\n",
       "year                                                                          \n",
       "1974          0.127915                0.021201           0.021908  0.142049   \n",
       "1975          0.115880                0.032189           0.040057  0.131617   \n",
       "1976          0.145378                0.023994           0.021877  0.139732   \n",
       "1977          0.111186                0.028475           0.025085  0.122712   \n",
       "1978          0.128788                0.017218           0.014463  0.096419   \n",
       "\n",
       "polviews  Moderate  Slightly conservative  Slightly liberal  \n",
       "year                                                         \n",
       "1974      0.380212               0.157597          0.149117  \n",
       "1975      0.386266               0.145923          0.148069  \n",
       "1976      0.398024               0.147495          0.123500  \n",
       "1977      0.402712               0.164746          0.145085  \n",
       "1978      0.384986               0.182507          0.175620  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtab_norm = pd.crosstab(year, polviews7, normalize=\"index\")\n",
    "xtab_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "We are almost ready to plot the results, but first we need some colors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "## Color Palettes\n",
    "\n",
    "Seaborn provides a variety of color palettes,  [which you can read about here](https://seaborn.pydata.org/tutorial/color_palettes.html).\n",
    "\n",
    "To represent political views, I'll use a diverging palette from blue to red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = sns.color_palette(\"RdBu_r\", 7)\n",
    "sns.palplot(palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "Here's the modified diverging palette  with purple in the middle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette[3] = \"purple\"\n",
    "sns.palplot(palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "source": [
    "A feature of this color map is that the colors are meaningful, at least in countries that use blue, purple, and red for these points on the political spectrum.\n",
    "A drawback of this color map is that some some of the colors are indistinguishable to people who are [color blind](https://davidmathlogic.com/colorblind).\n",
    "\n",
    "Now I'll make a dictionary that maps from the responses to the corresponding colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"Extremely liberal\",\n",
    "    \"Liberal\",\n",
    "    \"Slightly liberal\",\n",
    "    \"Moderate\",\n",
    "    \"Slightly conservative\",\n",
    "    \"Conservative\",\n",
    "    \"Extremely conservative\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": [
     "remove-print"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extremely liberal (0.16339869281045757, 0.44498269896193776, 0.6975009611687812)\n",
      "Liberal (0.4206843521722416, 0.6764321414840447, 0.8186851211072664)\n",
      "Slightly liberal (0.7614763552479817, 0.8685121107266438, 0.924567474048443)\n",
      "Moderate purple\n",
      "Slightly conservative (0.9824682814302191, 0.8006920415224913, 0.7061130334486736)\n",
      "Conservative (0.8945790080738177, 0.5038062283737024, 0.39976931949250283)\n",
      "Extremely conservative (0.7284890426758939, 0.15501730103806227, 0.1973856209150327)\n"
     ]
    }
   ],
   "source": [
    "color_map = dict(zip(columns, palette))\n",
    "\n",
    "for key, value in color_map.items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Cross Tabulation\n",
    "\n",
    "To see how the fraction of people with each political alignment has changed over time,\n",
    "we'll use `plot_series_lowess` to plot the columns from `xtab_norm`.\n",
    "\n",
    "Here are the 7 categories plotted as a function of time.\n",
    "The `bbox_to_anchor` argument passed to `plt.legend` puts the legend outside the axes of the figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, column in xtab_norm.items():\n",
    "    plot_series_lowess(column, color_map[name])\n",
    "\n",
    "decorate(\n",
    "    xlabel=\"Year\",\n",
    "    ylabel=\"Proportion\",\n",
    "    title=\"Fraction of respondents with each political view\",\n",
    ")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way of looking at the results suggests that changes in political alignment during this period have generally been slow and small.\n",
    "The fraction of self-described moderates has not changed substantially.\n",
    "The fraction of conservatives increased, but seems to be decreasing now; the number of liberals seems to be increasing.\n",
    "\n",
    "The fraction of people at the extremes has increased, but it is hard to see clearly in this figure.\n",
    "We can get a better view by plotting just the extremes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_columns = [\"Extremely liberal\", \"Extremely conservative\"]\n",
    "\n",
    "for name, column in xtab_norm.items():\n",
    "    if name not in selected_columns:\n",
    "        continue\n",
    "    plot_series_lowess(column, color_map[name])\n",
    "\n",
    "decorate(\n",
    "    xlabel=\"Year\",\n",
    "    ylabel=\"Proportion\",\n",
    "    ylim=[0, 0.057],\n",
    "    title=\"Fraction of respondents with extreme political views\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used `ylim` to set the limits of the y-axis so it starts at zero, to avoid making the changes seem bigger than they are.\n",
    "\n",
    "This figure shows that the fraction of people who describe themselves as \"extreme\" has increased from about 2.5% to about 5%.\n",
    "In relative terms, that's a big increase.  But in absolute terms these tails of the distribution are still small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Let's do a similar analysis with `partyid`, which encodes responses to the question:\n",
    "\n",
    ">Generally speaking, do you usually think of yourself as a Republican, Democrat, Independent, or what?\n",
    "\n",
    "The valid responses are:\n",
    "\n",
    "```\n",
    "0\tStrong democrat\n",
    "1\tNot str democrat\n",
    "2\tInd,near dem\n",
    "3\tIndependent\n",
    "4\tInd,near rep\n",
    "5\tNot str republican\n",
    "6\tStrong republican\n",
    "7\tOther party\n",
    "```\n",
    "\n",
    "You can read the codebook for `partyid`  at <https://gssdataexplorer.norc.org/variables/141/vshow>.\n",
    "In the notebook for this chapter, there are some suggestions to get you started."
   ]
  },
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    "Here are the steps I suggest:\n",
    "\n",
    "1) If you have not already saved this notebook, you should do that first.  If you are running on Colab, select \"Save a copy in Drive\" from the File menu.\n",
    "\n",
    "2) Now, before you modify this notebook, make *another* copy and give it an appropriate name.\n",
    "\n",
    "3) Search and replace `polviews` with `partyid` (use \"Edit->Find and replace\").\n",
    "\n",
    "4) Run the notebook from the beginning and see what other changes you have to make.\n",
    "\n",
    "You will have to make changes in `d7` and `columns`.  Otherwise you might get a message like \n",
    "\n",
    "`TypeError: '<' not supported between instances of 'float' and 'str'`\n",
    "\n",
    "Also, you might have to drop \"Other party\" or change the color palette.\n",
    "\n",
    "And you should change the titles of the figures.\n",
    "\n",
    "\n",
    "What changes in party affiliation do you see over the last 50 years?  Are things going in the directions you expected?"
   ]
  },
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   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This chapter uses some tools we have seen before, like the `Pmf` object and the `groupby` function.\n",
    "And it introduces two new tools: local regression for computing a smooth curve through noisy data, and cross tabulation for counting the number of people, or fraction, in each group over time.\n",
    "\n",
    "Now that we have a sense of how political alignment as changed, in the next chapter we'll explore the relationship between political alignment and other beliefs and attitudes."
   ]
  },
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   "metadata": {
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   "source": [
    "Political Alignment Case Study\n",
    "\n",
    "Copyright 2020 Allen B. Downey\n",
    "\n",
    "License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
   ]
  }
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